publication . Article . 2017

Detection and visualization of misleading content on Twitter

Markos Zampoglou; Christina Boididou; Lazaros Apostolidis; Yiannis Kompatsiaris; Olga Papadopoulou; Symeon Papadopoulos;
Open Access
  • Published: 04 Dec 2017 Journal: International Journal of Multimedia Information Retrieval, volume 7, pages 71-86 (issn: 2192-6611, eissn: 2192-662X, Copyright policy)
  • Publisher: Springer Science and Business Media LLC
The problems of online misinformation and fake news have gained increasing prominence in an age where user-generated content and social media platforms are key forces in the shaping and diffusion of news stories. Unreliable information and misleading content are often posted and widely disseminated through popular social media platforms such as Twitter and Facebook. As a result, journalists and editors are in need of new tools that can help them speed up the verification process for content that is sourced from social media. Motivated by this need, in this paper, we present a system that supports the automatic classification of multimedia Twitter posts into cred...
Persistent Identifiers
free text keywords: Media Technology, Library and Information Sciences, Information Systems, verication, fake detection, information credibility, Fake news, Information retrieval, Social media, Misinformation, Visualization, End user, Real image, Retraining, Multimedia information systems, Computer science
Funded by
REVEALing hidden concepts in Social Media
  • Funder: European Commission (EC)
  • Project Code: 610928
  • Funding stream: FP7 | SP1 | ICT
In Video Veritas – Verification of Social Media Video Content for the News Industry
  • Funder: European Commission (EC)
  • Project Code: 687786
  • Funding stream: H2020 | IA
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